It is well known that computer technology can be effectively employed for financial applications. It is also known to employ computers that execute optimization programs, such as programs based on linear programming techniques, so as to achieve financial goals. For example, computer technology that analyzes and optimizes a portfolio held by a given entity is known. Computer systems have also been employed as an intermediary in transactions where multiple parties desire to trade specific equity instruments. In such computer applications, optimization may be employed to facilitate trading of an equity of interest. However, the inventors are not aware of computer technology developed for trading holdings of multiple participants, where a computer acting as an intermediary processes entire portfolios of the participating entities and generates trades that optimize portfolios for a desired result, particularly for portfolios of fixed income instruments.
Portfolio-based trading, for example, exists in the equities market, where investors may buy or sell a portfolio of stocks on an aggregate basis. The investor provides a statistical description of the portfolio, usually including how closely it tracks the S&P 500 index, the sector distribution of the portfolio, and a measure of the diversification of the portfolio. The broker then commits to trade the portfolio of unknown stocks for a fixed fee at the prevailing market price at a pre-arranged point in time, typically the market daily close. Because the broker only knows the “statistical” composition of the portfolio, the investor feels more comfortable that the broker is unable to affect the closing prices. Because of the statistical relationship between the portfolio and the index, the broker feels comfortable that the investor cannot unload a portfolio of unattractive securities. An important component of such a transaction is the independent price of equities contributed by the public transaction records of the equity markets.
The vast majority of fixed income transactions are performed on a principal basis where the broker takes the opposite side of the transaction from the investor. The lack of adequate fixed income transaction records and the broad range of structures and maturities of fixed income instruments creates a significant barrier to developing the confidence on either side of the transaction that pricing is fair. Thus, it is desirable to provide a system that employs unbiased pricing and reassures the investors that the transaction is a fair deal. Further, it is desirable to provide computer technology that supports such fixed income transactions and, in particular, enables multiple parties to participate in the transactions. In particular, it is desirable to develop computer technology that would allow multiple investors to specify constraints on their portfolio holdings and, within those constraints, allocate by the optimization computer process fixed income holdings to individual investors participating in the transaction.
As noted, in general, optimization techniques for financial applications are known. For example, Adamidou et al., Financial Optimization, S. A. Zenios, Ed., Cambridge University Press, Cambridge, 1993, describe the Prudential-Bache Optimal Portfolio System, based on linear optimization of security holdings. This system emphasizes “scenario analysis,” which involves the evaluation of stochastic price models over user views of volatility employing a linear programming optimization constrained by duration, convexity, and return of holdings.
Optimization methodologies relating to financial applications are surveyed in H. Dahl, A. Meeraus, and S. A. Zenios, Some Financial Optimization Models: I Risk Management, Financial Optimization, S. A. Zenios, Editor, Cambridge University Press, Cambridge, 1993; and in H. Dahl, A. Meeraus, and S. A. Zenios, Some Financial Optimization Models: II Financial Engineering, Financial Optimization, S. A. Zenios, Editor, Cambridge University Press, Cambridge. 1993. Linear programs are described for general immunization of liabilities with fixed-income securities and “dedication” matching of assets to liabilities. The discussed programs become mixed-integer programs if round lots are to be traded. Mixed-integer programs are discussed for optimal settlement of financial forwards in a specific case of mortgage-backed securities and for optimal structuring of collateralized mortgage obligations.
Such publications on financial engineering do not teach computer technology that enables multi-party portfolio trading in fixed income instruments, wherein computer-driven optimization aids in rebalancing portfolios of multiple participants. Yet, there is a need for such technology. For example, there is a need to provide computer technology that enables multiple investors to recognize the economic benefits of selling bonds at a price below the price originally paid thereby obtaining a tax deduction. Accordingly, there is a need to develop technology that would enable investors to exchange portfolio holdings so as to substantially maximize the tax deductible loss. It is believed that technology for such portfolio trading between multiple parties that enables them to substantially optimize trades so as to substantially maximize tax advantages has not been developed by others.